From the course: Learning Amazon Web Services (AWS) QuickSight

Removing fields - AWS QuickSight Tutorial

From the course: Learning Amazon Web Services (AWS) QuickSight

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Removing fields

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- [Instructor] By selecting to only keep the fields or columns we need during the ETL process, we improve our query performance because we're using a smaller dataset. Let's navigate to our 2020 California dataset. Again let's select to edit the dataset. On the left hand side of the screen notice that you can select to include, or by unchecking the box, remove or exclude the field from the dataset. You can also see how fewer columns can make it easier to read the dataset and manage because we're working with fewer fields. You can select all of the data fields by selecting all at the top of the field list. Or you can select none by selecting the none option next to it. If you have a lot of fields and you're looking for a specific field name, you can use the search functionality above the field list right above the calculated fields section so search for a particular field. Let's select temperature. We see the options for temperature that match pop up in the field list below. This can save time if you're dealing with a large dataset and you're trying to find a particular one. You can also select to include or exclude data fields by selecting the down arrow, the toggle button, and select to include field. Let's include all our fields then decide which ones we want to remove. Let's select to clear our temperature search so we see all the fields in the data table that we brought in with the CSV file. I'm going to deselect to use the station because we already have a location name that's going to mean a lot more to us than the station name. So we uncheck the box next to station. Conversely I'm also going to remove these attribute fields. Because if we look at the dataset, they're giving us some information, but it's not necessarily data that we're looking for. So we can again deselect the attribute options. We now have a much more efficient data table that's easier to read and more importantly will work faster because we're importing a smaller dataset into Spice that's going to return our results and be much quicker to our analysis and calculations.

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